Introduction
Do public schools get better access to public internet? This research analyses whether public schools in Panama, get access to public internet via access points - Google Satellite - Migration Panama
- meter data temporal de uso de datos por access points
- agregar datos de cantidad de estudiantes por distrito https://www.inec.gob.pa/archivos/P030194820231213142523Cuadro%2021.pdf
- agregar un indice normalizado (ya que el numero es muy chiquito) de la cantidad de casas sin internet https://www.inec.gob.pa/archivos/P0705547520240202111515Cuadro%201.pdf
Future: - indicadores socioeconomicos a nivel de distrito https://www.inec.gob.pa/archivos/P0579518620240202083001Cuadro%204.pdf
dijo:
It’s the remoteness of Oyala that makes it so appealing to President Obiang. In a rare interview he described how rebels had recently plotted a seaborne assault on his palace in the current capital, Malabo. ‘We need a secure place for my government and for future governments. That’s why we have created Oyala, to guarantee the government of Equatorial Guinea.’ (Sackur 2012)
This case is far from exceptional, as an even more recent Washington Post article points out with respect to Myanmar’s decision to move its capital from Yangon to Naypyidaw:
Analysts have described the decision as motivated by a desire to secure the military’s seat of power from any threat of protests or invasions. (Berger 2021)
Most of these studies, however, are based on observations of conflict events. In this study, we study the more fundamental variable of a capital’s distance from the population centroid of the country.
Literature Review
Campante, Do, and Guimaraes (2019) analyzes the relationship between the location of a capital city and the degree of conflict and misgovernance in a given country. Their two key findings are that:
Conflict is more likely to emerge (and dislodge incumbents) closer to the capital
and
Isolated capitals are associated with misgovernance.
This first finding is illustrated in ?@fig-conflict-dist
Exploratory Data Analysis (EDA)
Below we display two tables for both ** Districts and at the Province level ** showing relevant indicators such as: - Numbers of schools - Numbers of Access Points - Population - Access Points per School Ratio - Access Points per 1000 people Ratio
We can see from the above tables, that Districts that house cities, are the ones with higher amounts of access points. We see the same behavior with the Provinces, specially with main cities such as Panama, Veraguas and Chiriqui.
Most of these studies, however, are based on observations of conflict events. In this study, we study the more fundamental variable of a capital’s distance from the population centroid of the country.
Correlations between Population, Schools and Access Points
Below I show 2 plots, where on the left we see the relationship between Access points and Population and on the right, we see the relationship between Access Point-School ratio with the Population.
Statistical Regression of Access Points on Population
I have run 2 statistical regression models, where I regress Access Points on Population and Access Points both on Population and Schools. Adding schools improves the model’s explanatory power (R² increased by 2.6%). Both population and schools are significant predictors as we can see in the below tables. We can see that for each additional school, we expect 0.216 more access points, holding population constant
| Model 1 | Model 2 | |
|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | ||
| Intercept | 4.874*** | -2.568 |
| (1.302) | (1.772) | |
| Population | 0.000*** | 0.000*** |
| (0.000) | (0.000) | |
| Schools | 0.216*** | |
| (0.040) | ||
| Num.Obs. | 75 | 75 |
| R2 | 0.909 | 0.935 |
| R2 Adj. | 0.907 | 0.933 |
Geospatial Analysis
Access Points Map
Let’s explore how access points are distributed along the country. As validated above, the districts with the highest amount of access points are those located around Panama City (459 approx), Santiago with 198 and Boquete with approximately 118 access points. Interestingly we can detect that probably these clusters, do not follow a random location.
Access Points Density per District Map
Now let’s explore how different access points density is within districts. With this map we can confirm that the districts of David, Santiago, Colon and Panama are the ones with a higher concentration of access points. This makes sense in the context that, these districts are where we can locate higher economic development in the country.
Panama Highway Map
Highways represent economic development, as they try to bridge different disitrcts across the country. Our hypothesis here is that, access points will be located, around districts where we can see an intersection with the Panamerican Highway. It is worth noting that this highway goes across the complete country form west to east, mostly located on the pacific side of the country, where Panama City is.
Public Schools Map
This map shows, clustered, every school in the country. There are 9 types of classifications on the school system:
- COIF
- Cefacei
- IPHE
- Kinder
- Parvulario
- Primaria Oficial
- Privada
- Secundaria
- Universidad
Which Districts are Intersected by the Panamerican Highway?
Below we find, which are the districts that intersect with the Panamerican Highway. As confirmed by the above analysis, the districts with the higher population densities and with the higher amount of access points installed, are more likely to be intersected by it. This is a causal effect of infrastructure development, as highways are more likely to be equiped with better structures to implement different services such as telecommunications.
Moran’s I
Access Points Moran’s I
We know that the scale runs from -1 (perfect dispersion) to +1 (perfect clustering). Our result is 0.229, which indicates positive spatial correlation meaning that districts with similar number of access points (higher or lower) tend to cluster together. With our p-statistic, we can reject the null hypothesis of random spatial distribution. This means that access points are not positioned randomly but rather logically.
[1] "Moran's I for Access Points vs Schools:"
Moran I test under randomisation
data: district_data_complete$access_points
weights: nb2listw(nb, style = "W", zero.policy = TRUE)
n reduced by no-neighbour observations
Moran I statistic standard deviate = 4.7929, p-value = 8.218e-07
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.229408902 -0.013888889 0.002576752 Mapping Access Points vs Schools ratio
As expected, we have a higher ratio within districts that are city districts such as Panama, San Miguelito, Pedasi and Chitre. It is worth mentioning that for instance, Pedasi is not a big city town, but instead is touristy town. They have 9 school total, with 11 AP located in this district.
Pairwise Intensity function
Analyzing Intensity function
As expected, we see that access points tend to cluster together within distances of 1500 meters. When that threshold is passed, the clustering of these points decreases sharply. This is consonant to the logic of installing access points in dense areas and where more schools are located.
Mean nearest neighbor distance: 1617.98 meters
Median nearest neighbor distance: 400.99 metersBuffer Analysis with Panamerican Highway
As expected, from this analysis, we can see that there is a higher amount of schools outside the inner buffers, as schools will try to serve the wider population, beyond the urban areas. On the other hand, we will see more access points located in urban areas, therefore closer to the highway as they serve more the densed urban areas.